ORIGINAL PAPER

Comparison of Kalpana-1 atmospheric motion vectorswith other observations

S. K. Deb & Inderpreet Kaur & C. M. Kishtawal & P. K. Pal

Received: 29 January 2013 /Accepted: 9 May 2013# Springer-Verlag Wien 2013

Abstract The operational derivation of atmospheric motionvectors (AMVs) using infrared (10.5–12.5 μm) and watervapor (6.3–7.1 μm) channels of successive geostationarysatellite images started in the 1980s. Subsequently, AMVshave become an important component for operationalnumerical weather prediction throughout the globe for the lastdecade or so. In India, at the Space Applications Centre,Indian Space Research Organisation, the operational deriva-tion of AMVs (infrared winds and water vapor winds) fromthe Indian geostationary satellite Kalpana-1 has been initiateda few years back. Recently, an L-band radar lower atmospherewind profiler (LAWP) has been installed at the NationalAtmospheric Research Laboratory, Gadanki located at(13.58°N, 79.28°E) for continuous high-resolution wind mea-surements in the lower atmosphere. In this study, a compari-son of Kalpana-1 AMVs with wind measurements fromLAWP and radiosonde has been carried out for a period ofone and a half years. The performances of Kalpana-1 AMVsare also assessed by a separate comparison of Meteosat-7AMVs, derived at the European Organisation for theExploitation of Meteorological Satellites, with wind measure-ments from LAWP and radiosonde. Both sets of comparisonshow that AMVs from Kalpana-1 and Meteosat-7 are compa-rable over the Indian Ocean region.

1 Introduction

The Space Applications Centre (SAC), Indian Space ResearchOrganisation (ISRO), has started operationally producing

atmospheric motion vectors (AMVs; infrared winds and watervapor winds) using three successive Kalpana-1 infrared andwater vapor images two times a day (00 UTC and 12 UTC)since June 2007 (Deb et al. 2008; Kishtawal et al. 2009) underthe INSAT Meteorological Data Product System project.Subsequently, after 2 years when the 30-min image acquisi-tion of Kalpana-1 was operationalized, AMVs from Kalpana-1 are being produced every half an hour. Later upon thecompletion of a specific validation campaign, some changeshave been made to the operational algorithm, specifically theuse of multiple images in place of three images for qualitycontrol procedure (Deb et al. 2012) and the implementation ofwidely tested height assignment (Deb et al. 2013) techniques(viz. infrared window technique, H2O intercept method, cloudbase method, etc.). The accuracies of AMVs are generallydetermined by comparing the satellite-derived winds withmaximum available radiosonde measurement; however, veryfew studies are available in the literature where AMVs arecompared with winds measured from an atmospheric windprofiler (Bedka et al. 2009) and dropsonde measurement froma research aircraft (Sears and Velden 2012). Recently, an L-band radar lower atmosphere wind profiler (LAWP) wasinstalled at the National Atmospheric Research Laboratory(NARL), Gadanki located at (13.58°N, 79.28°E), India, forcontinuous high-resolution wind measurements in the loweratmosphere (Srinivasulu et al. 2012). The establishment of thisL-band radar at NARL has given an opportunity to inter-compare AMVs derived from a geostationary satellite withwind measurement from LAWP. However, before proceedingto the inter-comparison, it should be noted that winds from ageostationary platform are inherently not able to depict a truewind at any particular point or level of the atmosphere, even ifthe optimum tracer characteristic and time behavior are in-cluded in the algorithm. The main assumptions of satellite-derived motion winds are (a) features move within a shortinterval of time at a particular level, (b) features do not changeshape and acceleration, and (c) features move with speedequal to the true winds at any given level for a specific time

S. K. Deb (*) : I. Kaur :C. M. Kishtawal : P. K. PalAtmospheric and Oceanic Sciences Group, Space ApplicationsCentre, Indian Space Research Organisation (ISRO), Ahmedabad380015, Indiae-mail: sanjib_deb@rediffmail.com

S. K. Debe-mail: sanjib_deb@sac.isro.gov.in

Theor Appl ClimatolDOI 10.1007/s00704-013-0926-9

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interval of image sequence used for retrieval. The first twoassumptions can be violated in a convective cloud system ifthe image sequence interval is large enough compared to highvertical motions. It is difficult to verify the third assumption asno instrument is currently available to measure the true windsbecause of instrument errors and small-scale variability in theatmosphere. Studies have shown that winds from a geosta-tionary platform better correspond to a layer of atmosphericdepth (Velden and Bedka 2009). The wind profile measure-ments from in situ radiosonde and wind profilers can also beeffected by vertical momentum transports occurring in cloud-free regions because of cumulus cloud dynamics but still offerstable, well-calibrated datasets for comparison. The mainadvantage of wind profilers is that they can provide high-temporal-resolution observations. The winds from radio-sonde measurement may be slightly more accurate com-pared to profiler winds, but a major limitation is the lessnumber of observations. The main objective of the presentstudy is to compare the Kalpana-1 infrared and watervapor winds (together called as AMVs) with that ofLAWP wind measurement and radiosonde winds. To geta fair idea about the performance of Kalpana-1 AMVsover the Indian Ocean region, a similar comparison is alsodone with Meteosat-7 AMVs derived at the EuropeanOrganisation for the Exploitation of MeteorologicalSatellites (EUMETSAT). The following Section 2 describesthe data used, while Section 3 presents the methodology forcollocation and the validation results. Finally, Section 4 givesthe conclusion from this study.

2 Data

This study uses three types of wind measurement, viz. (a)satellite-derived winds, (b) radar winds, and (c) radiosondewinds. A brief outline about the three types of winds usedfor this study is as follows:

(a) Satellite-derived winds: The winds derived from theinfrared and water vapor channels (i.e., together calledas AMVs) of Kalpana-1 and Meteosat-7 in the region(0–130°E, 50°S–50°N) for the period 1 July 2010 to 31December 2011 are used. Kalpana-1 AMVs are avail-able every 30 min from the operational website (http://www.mosdac.gov.in), while Meteosat-7 AMVs areavailable every 90 min from EUMETSAT (http://www.eumetsat.in). The operational algorithm used toderive winds from Kalpana-1 is described in Kishtawalet al. (2009), with recent changes in height assignment(Deb et al. 2013) and quality control (Deb et al. 2012)techniques. A preliminary validation of Kalpana-1winds using radiosonde data is also described in Debet al. (2012). Since the operational algorithm was

updated very recently, the older Kalpana-1 winds usedin this study are reprocessed offline as per the recentchanges in the algorithm. The status and retrieval algo-rithm of Meteosat-7 winds is given very briefly inHolmhund et al. (2004). Both AMV datasets available at00 UTC, 03 UTC, 06 UTC, 09 UTC, 12 UTC, 15 UTC,18 UTC, and 21 UTC are used for this study.

(b) Radar winds: The lower atmospheric winds are derivedfrom the 1,280-MHz active array radar LAWP installedat NARL, Gadanki (Srinivasulu et al. 2011, 2012). Themain objective of installing this radar is to get contin-uous wind measurements for analyzing differentweather phenomena in the lower atmosphere. Theheight assignment of satellite-derived winds is donein terms of pressure (in hPa), while wind profiles fromLAWP are measured in terms of height (in meters). Fora direct comparison, the height in altitude (i.e., inmeters) of the profiler data is converted to pressureheight. The maximum available wind data measuredfrom LAWP at Gadanki throughout a particular dayfrom 1 July 2010 to 31 December 2011 are taken forthis study. A time–height display of the wind profilefrom LAWP for 12 July 2011 is shown in Fig. 1. Itshows westerly to southwesterly winds typical duringthe summer monsoon season and a diurnal variation ofthe horizontal wind vector. It also shows that the pro-filer is capable of measuring the wind vector consis-tently up to 4–6 km of height.

(c) Radiosonde winds: The radiosonde wind observationsavailable from the NOAA real-time as well as archivewebsite (http://www.esrl.noaa.gov/raobs/) for theregion (0–130°E, 50°S–50°N) are used for this study.The real-time archive comprises all global GTS observa-tions. All the radiosonde wind measurements for theperiod 1 July 2010 to 31 December 2011 are considered.The maximum radiosonde data are available throughoutthe globe at 00 UTC and 12 UTC time; hence, measure-ments at 00 UTC and 12 UTC are used for this study.Figure 2 shows a typical example of radiosonde pointsused for collocation in the area of interest for a particularpoint of time.

3 Collocation methodology and results

The accuracy of the wind profile measured from LAWP isestablished (Srinivasulu et al. 2012) for a limited period;however, for the sake of completeness, the winds fromLAWP are independently validated with the two nearestavailable radiosonde observations at the locations(77.58°E, 12.97°N) and (80.18°E, 13.00°N) for the entirestudy period. Once the accuracy of the wind profile fromLAWP is well established for a considerably long period,

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three sets of comparisons are made, where satellite winds(Kalpana-1 infrared and water vapor winds separately) arecollocated with the (a) wind profile from radiosonde, (b) windprofile measured from LAWP, and (c) wind profile fromradiosonde and LAWP together (i.e., collocation of three typesof winds). Finally, to assess the performance of Kalpana-1winds, similar sets of comparisons are repeated for Meteosat-7winds. The collocation criteria for each comparison are givenin Table 1. During collocation, the satellite winds with a speeddifference of 30 m/s and a direction difference of 60° withrespect to the observed winds (i.e., radiosonde or LAWP

winds) are not considered for validation. A total of around7–10 % of cases lie beyond this category for this period ofvalidation. The statistics used in the comparison of thesedatasets are the speed root-mean-squared difference(SRMS), directional RMS (DRMS), wind speed bias anddirection bias respectively. In this study, the averaged statisticsfor the whole study period (i.e., 18 months) as well as for fourdifferent seasons are also generated. The seasonal averages arerepresented as JJA (June–July–August), SON (September–October–November), DJF (December–January–February),and MAM (March–April–May).

Fig. 1 A sample time–heightdisplay of the wind profile fromLAWP for 11 July 2011 atNARL, Gadanki

Fig. 2 A typical example ofradiosonde data network usedfor validation purpose

Comparison of Kalpana-1 AMVs with other observations

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3.1 Comparison: winds from LAWP and radiosonde

For the comparison of winds between LAWP and radio-sonde measurements, the wind profiles are collocated withina spatial difference of 1° along the longitude and latitudeand a vertical difference of 10 hPa. Since the majority ofradiosonde data are available at 00 UTC and 12 UTC, atemporal difference of ±1.5 h with respect to 00 UTC and 12UTC is considered for collocations. Table 2 shows the RMSand bias in speed and direction along with the mean speedand direction of radiosonde winds. It is observed that SRMSand DRMS are 3.41 m/s and 34.6°, respectively, with bias inspeed and direction of 2.43 m/s and −1.9°, respectively. Thevalues of the four seasonal averages are also not far awayfrom each other, while the maximum SRMS is 4.04 m/sduring MAM and the minimum value is 1.82 m/s duringDJF. Also, the DRMS values for the four seasons are almostthe same except for MAM. This close agreement of windsbetween LAWP and radiosonde is also examined by

accuracies of the profiler data with height. The vertical pro-files of SRMS (Fig. 3a) and DRMS (Fig. 3b) indicate thatSRMS is ranging between 2 and 5 m/s up to 550 hPa, whichthereafter increases to 7 m/s at 300 hPa, while DRMS isranging between 30° and 50° for the entire vertical range.The seasonal vertical profiles are also not very different;however, the vertical extent during DJF is up to 600 hPa,while that during MAM is 750 hPa. The low vertical extentduring DJF and MAM is due to the less number of colloca-tions for these seasons, which is evident from Table 2. Thetotal number of collocations is 2,104 for the entire studyperiod of one and a half years. The height match histogramof LAWP winds collocated with radiosonde winds for dif-ferent levels is shown in Fig. 3c. It shows that 75 % of thetotal collocated match points are within near surface to700 hPa. The larger errors in the upper level can be attrib-uted to the increased profiler instrument error due to a poorsignal-to-noise ratio (Srinivasulu et al. 2011) and decreasedvertical resolution of the high-mode profiler data whichcannot capture the strong vertical wind shear which occursnear the tropopause.

3.2 Comparison: winds from satellite and radiosonde

In this section, the comparison of satellite winds with radio-sonde is discussed. The satellite-derived winds (infrared andwater vapor winds) from Kalpana-1 and Meteosat-7 arecollocated separately with radiosonde observations withina spatial difference of 1° and a vertical difference of 50 hPa.The temporal match of satellite winds and radiosonde windscoincides exactly in time (i.e., 00 UTC and 12 UTC). Thecollocated sample size is quite large because of the largercoverage of radiosonde data. The RMS and bias of windspeed and direction of the infrared and water vaporwinds of Kalpana-1 and Meteosat-7 are shown inTable 3. It is observed from the table that the SRMS

Table 1 The different criteria for matching satellite-derived winds and the wind profile from LAWP and radiosonde for statistical comparisons

Dataset compared Match criterion

Horizontal (deg) Vertical (hPa) Temporal

Comparison 1: collocation of winds between LAWP winds and radiosonde winds

Wind profile from LAWP and radiosonde <1 <10 ±1.5 h; 00 UTC and 12 UTC radiosonde data

Comparison 2: collocation of winds between satellite winds and radiosonde winds

Satellite-derived winds and winds from radiosonde <1 <50 Exact match at 00 UTC and 12 UTC

Comparison 3: collocation of winds between satellite winds and wind profile from LAWP

Satellite-derived winds and wind profile from LAWP <1 <50 ±1.5 h for every 3 h

Comparison 4: collocation of winds between satellite winds and wind profile from LAWP and radiosonde

Satellite-derived winds and winds from radiosonde <1 <50 Exact match at 00 UTC and 12 UTC

Satellite-derived winds and wind profile from LAWP <1 <50 ±1.5 h of satellite winds at 00 UTC and 12 UTC

Wind profile from LAWP and radiosonde <1 <50 ±1.5 h of radiosonde winds at 00 UTC and 12 UTC

Table 2 The statistical parameters calculated when winds from LAWPare collocated with radiosonde

LAWP vs. RS

ALL JJA SON DJF MAM

SRMS 3.41 4.00 2.97 1.82 4.04

DRMS 34.6 35.9 32.75 39.8 19.02

Speed bias 2.43 2.79 2.39 1.1 2.06

Direction bias −1.90 −5.71 4.17 −17.95 −14.16

Mean speed RS 7.74 9.45 6.96 5.38 5.20

Mean direction RS 241.4 274.68 230.47 188.62 228.34

Collocations 2,104 653 793 362 296

The unit for speed RMS and bias is meters per second, while that fordirection is degrees

RS radiosonde, SRMS speed RMS, DRMS direction RMS

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of the infrared and water vapor winds of Kalpana-1 are 5.11and 6.2 m/s, respectively, for the whole validation period,while those of Meteosat-7 are 5.6 and 6.1 m/s, respectively.The DRMS of the infrared and water vapor winds of Kalpana-1 are 20.1° and 17.7°, respectively, while those of Meteosat-7are 21.6° and 18.7°, respectively. The seasonal average of

SRMS and DRMS for the infrared and water vapor winds ofboth satellites is comparable. The bias in speed and directionof Kalpana-1 and Meteosat-7 are not very different; however,the number of collocations in Kalpana-1 is a bit higher ascompared to the collocation points in Meteosat-7. The meanspeed and direction of radiosonde winds are also shown. The

Fig. 3 The vertical profiles of a speed RMS, b direction RMS, and c height match histograms, when wind measurements from LAWP arecollocated with radiosonde observations

Table 3 The statistical parameters calculated when the infrared and water vapor winds of Kalpana-1 and Meteosat-7 are collocated with radiosonde

IR winds vs. RS WV winds vs. RS

ALL JJA SON DJF MAM ALL JJA SON DJF MAM

Kalpana-1 winds (IR and WV) and RS

SRMS 5.11 4.65 5.11 5.4 5.2 6.2 5.9 5.8 6.3 5.99

DRMS 20.1 19.2 19.0 19.1 19.6 17.7 18.2 17.3 16.3 17.7

Speed bias 0.05 −0.2 0.16 0.6 0.19 0.71 0.8 0.55 0.91 0.78

Direction bias −0.05 1.35 −0.19 −1.3 −1.4 0.91 2.6 1.72 −1.25 −0.92

Mean speed RS 13.42 12.51 13.5 15.0 14.2 19.82 16.4 18.8 20.7 18.5

Mean direction RS 199.5 182.24 198.0 209.8 211.3 187.99 147.7 187.7 189.7 195.6

Collocations 46,150 15,416 15,317 7,185 8,232 40,648 13,189 14,189 5,207 8,063

Meteosat-7 winds (IR and WV) and RS

SRMS 5.6 5.05 5.49 5.94 5.49 6.1 6.1 5.7 6.23 5.88

DRMS 21.6 21.52 20.43 19.02 19.58 18.7 19.44 18.35 17.2 17.2

Speed bias 2.9 2.7 2.79 3.31 2.98 2.7 3.09 2.44 2.6 2.94

Direction bias 0.45 −0.36 0.68 1.02 0.22 1.76 2.87 1.69 1.1 0.19

Mean speed RS 11.95 10.65 11.91 14.39 13.53 17.25 15.86 16.96 19.05 18.54

Mean direction RS 200.44 174.63 208.9 213.1 213.9 204.59 175.0 203.6 217.65 211.2

Collocations 38,594 11,942 15,249 6,037 5,366 39,849 12,360 15,412 6,685 5,392

The unit for speed RMS, bias, and mean speed is meters per second, while that for direction is degrees

IR infrared, WV water vapor, RS radiosonde, SRMS speed RMS, DRMS direction RMS

Comparison of Kalpana-1 AMVs with other observations

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vertical profiles of SRMS and DRMS of the infrared andwater vapor winds of Kalpana-1 and Meteosat-7 are shownin Figs. 4a, b and 5a, b. The vertical profiles of SRMS andDRMS of the infrared winds of Kalpana-1 and Meteosat-7(Fig. 4a, b) show that the values are comparable for bothsatellites, except for the lower level (i.e., 1,000 to 700 hPa)where Meteosat-7 infrared winds have higher SRMS, about1.5 m/s. Similarly, the vertical profiles of SRMS and DRMSof the water vapor winds (Fig. 5a, b) of both satellites arecomparable, except for the level 550–400 hPa where theDRMS of Kalpana-1 is a bit less compared to Meteosat-7values. The seasonal vertical profiles of both satellites are alsonot very different from each other. The height assignmentmatch histogram of the infrared winds of Kalpana-1 andMeteosat-7 collocated with radiosonde observations is shownin Fig. 4c, and that of water vapor winds is shown in Fig. 5c. Itis seen from the figures that the maximum collocations arefound in the region of the upper troposphere; in the case ofinfrared winds, 83.3 % for Kalpana-1 and 68.2 % forMeteosat-7 match points are within the 100–400 hPa region,while in the case of water vapor winds, 89.9 % for Kalpana-1and 76.4 % for Meteosat-7 match points are within the 100–300 hPa region. Themaximummatch points in the upper levelindicate the accurate estimation of motion winds from thegeostationary satellite in the upper level.

3.3 Comparison: winds from satellite and LAWP

In this section, the satellite-derived winds and the windprofile from LAWP are compared within a spatial differenceof 1°, vertical difference of 50 hPa, and temporal differenceof ±1.5 h for every 3 h. Though the collocation is done forevery 3 h, the collocated points are reduced drastically

because the wind profile from LAWP is available at a singleobservation point (i.e., at Gadanki). The separate statisticsare generated for infrared and water vapor winds for bothsatellites. The RMS and bias of wind speed and direction ofthe infrared and water vapor winds of Kalpana-1 andMeteosat-7 are shown in Table 4. It is observed from thetable that the SRMS of the infrared and water vapor windsof Kalpana-1 for the whole validation period are 5.0 and6.7 m/s, respectively, while those of Meteosat-7 are 4.5 and6.4 m/s, respectively. The DRMS of the infrared and watervapor winds of Kalpana-1 are 49.9° and 29.1°, respectively,while those of Meteosat-7 are 91.0° and 29.6°, respectively.The speed bias of Kalpana-1 is a bit higher compared to thecorresponding value of Meteosat-7. The bias in directionand numbers of collocations of the infrared and water vaporwinds of both satellites are comparable. The seasonal aver-aged values of SRMS and DRMS of both satellites arecomparable. The close agreement of satellite winds andLAWP winds is also being examined with respect to height.The vertical profile of SRMS and DRMS of the infraredwinds of Kalpana-1 and Meteosat-7 (Fig. 6a, b) shows thatthe SRMS of both satellites are comparable, while theDRMS of Meteosat-7 is overestimated by about 20°–25°for almost all levels. The slightly higher SRMS and DRMSvalues in the upper troposphere may be attributed to theincrease in profiler measurement error with height.Similarly, the vertical profile of SRMS and DRMS of watervapor winds (Fig 7a, b) shows that the DRMS of bothsatellites are comparable, while the SRMS of Kalpana-1for the level 550–400 hPa is lower by about 1.0–1.5 m/scompared to Meteosat-7 values. The seasonal averagedvalues of SRMS and DRMS values of the infrared windsand water vapor winds of both satellites are not very

Fig. 4 The vertical profiles of a speed RMS, b direction RMS, and c height match histograms, when infrared winds from Kalpana-1 and Meteosat-7 are collocated with radiosonde observations

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different from the averaged values of the entire period of thestudy. The lowest SRMS value for infrared winds is noticedduring the DJF period for both satellites, while for water vaporwinds, the lowest SRMS value is noticed during the MAMperiod. The height match histogram of the infrared winds ofKalpana-1 and Meteosat-7 collocated with LAWP observa-tions is shown in Fig. 6c, while that of water vapor winds isshown in Fig. 7c. It is seen from the figures that the maximumcollocations are found in the region of the lower atmosphere;in the case of infrared winds, 56% for Kalpana-1 and 74% for

Meteosat-7 match points are within the 550–1,000 hPa region,while in the case of water vapor winds, 95 % for Kalpana-1and 70.8 % for Meteosat-7 match points are within the 400–600 hPa region. The maximum match points in the middle tothe lower level indicate the accurate estimation of winds fromLAWP in the middle to the lower level which is because ofless instrument error and increase in vertical resolution of low-mode profiler data. This comparison has demonstrated thepotential use of LAWP winds for the validation of satellite-derived atmospheric winds.

Table 4 The same as Table 3, but when the infrared and water vapor winds of Kalpana-1 and Meteosat-7 are collocated with LAWP winds

IR winds vs. LAWP WV winds vs. LAWP

ALL JJA SON DJF MAM ALL JJA SON DJF MAM

Kalpana-1 winds (IR and WV) and LAWP

SRMS 5.0 6.3 4.6 3.7 4.51 6.7 6.7 6.7 6.0 3.8

DRMS 49.9 59.2 40.2 42.6 68.1 29.1 25.1 30.3 34.3 26.3

Speed bias 4.6 5.9 4.2 3.3 4.3 6.6 6.7 6.7 5.4 3.56

Direction bias −17.0 −0.8 −21.9 −30.4 −33.1 −8.52 −4.2 −12.8 −18.1 9.51

Mean speed LAWP 5.0 5.3 4.8 5.1 4.4 4.9 5.5 4.7 4.9 4.7

Mean direction LAWP 191.3 198.9 188.1 181.4 194.9 187.1 201.8 181.9 169.6 200.1

Collocations 3,510 1,283 1,114 643 470 924 351 295 208 70

Meteosat-7 winds (IR and WV) and LAWP

SRMS 4.5 5.5 4.4 3.2 4.5 6.4 6.2 7.8 6.7 2.6

DRMS 91.0 84.8 90.5 91.6 90.2 29.6 31.2 27.2 29.1 28.4

Speed bias 1.5 2.6 2.2 0.52 0.99 4.12 3.04 7.36 4.53 −0.99

Direction bias −33.8 −27.9 −55.6 −27.1 −28.7 −7.05 −20.8 −9.4 −3.72 66.7

Mean speed LAWP 4.54 5.7 4.36 4.8 3.5 5.4 5.6 4.7 5.6 5.9

Mean direction LAWP 203.62 210.7 217.7 161.1 201.6 195.1 240.3 143.2 183.3 147.8

Collocations 3,039 675 708 952 704 724 359 178 157 30

The unit for speed RMS, bias, and mean speed is meters per second, while that for direction is degrees

IR infrared, WV water vapor, SRMS speed RMS, DRMS direction RMS

Fig. 5 The same as Fig. 4, but when water vapor winds from Kalpana-1 and Meteosat-7 are collocated with radiosonde

Comparison of Kalpana-1 AMVs with other observations

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3.4 Comparison: winds from satellite, LAWP, and radiosonde

The previous sections (3.1, 3.2 and 3.3) discussed the com-parison of Kalpana-1 and Meteosat-7 infrared and watervapor winds with radiosonde and winds from LAWP withreasonable accuracies. In the present section, a triple collo-cation is made, where satellite-derived winds (infrared andwater vapor winds of Kalpana-1 and Meteosat-7), radio-sonde winds, and LAWP winds are collocated together.Due to triple collocations, the number of collocated pointsis reduced as compared to other comparisons. Table 5 showsthe statistical comparison of the infrared winds of Kalpana-1and Meteosat-7 with winds from LAWP and radiosonde.The SRMS values of the pair-wise comparison are 3.44 m/sfor Kalpana-1 infrared winds and radiosonde winds,4.77 m/s for LAWP winds and radiosonde winds, and

4.14 m/s for Kalpana-1 infrared winds and LAWP winds,while those for DRMS are 57.9°, 68.7°, and 35.5°, respec-tively. The close SRMS value of the LAWP and radiosondemeasurement (Table 1) contributes to similar SRMS valuesof Kalpana-1 infrared winds–LAWP winds and Kalpana-1infrared winds–radiosonde winds comparisons in triple col-locations. Similarly, for the collocations of infrared winds ofMeteosat-7, radiosonde, and LAWP, the SRMS values of thepair-wise comparison are 4.8 m/s for Meteosat-7 infraredwinds and radiosonde winds, 3.41 m/s for LAWP and ra-diosonde winds, and 5.07 m/s for Meteosat-7 infrared windsand LAWP winds, while those for DRMS are 38.34°,57.98°, and 56.9°, respectively. The total number of collo-cations in the case of the Kalpana-1 comparison is 666,while that for Meteosat-7 is 1,501. The table also showsthe mean speed and direction of infrared winds, radiosonde

Fig. 7 The same as Fig. 6, but when water vapor winds from Kalpana-1 and Meteosat-7 are collocated with winds from LAWP

Fig. 6 The vertical profiles of a speed RMS, b direction RMS, and c height match histograms, when infrared winds from Kalpana-1 and Meteosat-7 are collocated with winds from LAWP

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winds, and LAWP winds for the collocations. Similar to theearlier table, the statistical comparison of the water vaporwinds of Kalpana-1 and Meteosat-7 with winds from LAWPand radiosonde, when all three types of winds are collocatedtogether, is shown in Table 6. During the collocations of thewater vapor winds of Kalpana-1, radiosonde winds, andLAWP winds, the SRMS values of the pair-wise comparisonare 7.9 m/s for Kalpana-1 water vapor winds with radio-sonde winds, 3.9 m/s for LAWP winds with radiosondewinds, and 8.8 m/s for Kalpana-1 water vapor winds withLAWP winds, while those for DRMS are 49.6°, 46.8°, and47.6°, respectively. In the case of Meteosat-7, the SRMSvalues of the pair-wise comparison are 7.0 m/s for Metaosat-7 water vapor winds with radiosonde winds, 5.5 m/s forLAWP and radiosonde winds, and 8.0 m/s for Meteosat-7water vapor winds with LAWP winds, while those for

DRMS are 70.7°, 50.0°, and 81.5°, respectively. The totalnumbers of collocations in the case of Kalpana-1 andMeteosat-7 water vapor winds are 811 and 333, respectively.

4 Conclusions

The derived motion winds from different geostationary sat-ellites give a good coverage of atmospheric winds especiallyover oceanic regions throughout the globe. These satellitewinds retrieved from the infrared and water vapor channelsof different geostationary satellites are being regularly usedby operational agencies for assimilation in the numericalmodel for real-time weather forecast (Soden et al. 2001;Velden et al. 1992, 1998; Deb et al. 2010), and these windsare also used for various atmospheric applications (Bedka

Table 5 The statistical parameters calculated during the triple collocation of satellite infrared (Kalpana-1 and Meteosat-7) winds with winds fromLAWP and radiosonde

Kalpana-1 IR winds, RS, and LAWP Meteosat-7 IR winds, RS, and LAWP

Kalpana-1 Meteosat-7

IR winds vs. RS LAWP vs. RS IR winds vs. LAWP IR winds vs. RS LAWP vs. RS IR winds vs. LAWP

SRMS 3.44 4.77 4.14 4.8 3.41 5.07

DRMS 57.9 68.7 36.5 38.34 57.98 56.9

Speed bias 0.24 2.46 2.71 −0.31 1.77 1.46

Direction bias 24.8 −26.2 −1.40 2.61 −32.9 −30.31

Collocations 666 666 666 1,501 1,501 1,501

IR winds RS LAWP IR winds RS LAWP

Mean speed 8.4 8.17 5.71 6.45 6.76 4.98

Mean direction 163.3 138.4 164.7 194.4 191.8 224.8

The unit for speed RMS, bias, and mean speed is meters per second, while that for direction is degrees

IR infrared, RS radiosonde, SRMS speed RMS, DRMS direction RMS

Table 6 The same as Table 5, but for the water vapor winds of Kalpana-1 and Meteosat-7

Kalpana-1 WV winds, RS, and LAWP Meteosat-7 WV winds, RS, and LAWP

Kalpana-1 Meteosat-7

WV winds vs. RS LAWP vs. RS WV winds vs. LAWP WV winds vs. RS LAWP vs. RS WV winds vs. LAWP

SRMS 7.9 3.9 8.8 7.0 5.53 8.0

DRMS 49.6 46.83 47.65 70.7 50.0 81.5

Speed bias 6.28 0.96 7.25 4.2 1.53 5.8

Direction bias 4.3 −36.27 −31.9 −29.2 −34.81 −65.03

Collocations 811 811 811 333 333 333

WV winds RS LAWP WV winds RS LAWP

Mean speed 12.1 5.8 4.85 7.0 5.5 8.09

Mean direction 150.0 145.6 181.9 70.78 50.0 81.5

The unit for speed RMS, bias, and mean speed is meters per second, while that for direction is degrees

WV water vapor, RS radiosonde, SRMS speed RMS, DRMS direction RMS

Comparison of Kalpana-1 AMVs with other observations

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and Mecikalski 2005). In order to provide an assurance of thequality of the operational infrared and water vapor winds ofKalpana-1 to the global users, the validation of Kalpana-1winds is performed against winds from other sources, viz.radiosonde, lower atmosphere wind profiler, and Meteosat-7.During satellite wind comparison with radiosonde winds(Table 3), the SRMS and DRMS values for Kalpana-1 infraredwinds are 5.11 m/s and 20.1°, respectively, while those forMeteosat-7 are 5.6m/s and 21.6°, respectively. For water vaporwinds, the SRMS and DRMS values for Kalpana-1 are 6.2 m/sand 17.7°, respectively, while those for Meteosat-7 are 6.1 m/sand 18.8°, respectively. The maximum collocations are ob-served in the upper troposphere. During satellite wind compar-ison with LAWP winds (Table 4), the SRMS and DRMSvalues for Kalpana-1 infrared winds are 5.0 m/s and 49.9°,respectively, while those for Meteosat-7 are 4.5 m/s and 91°,respectively. The SRMS and DRMS values for Kalpana-1water vapor winds are 6.7 m/s and 29.1°, respectively, andthose for Meteosat-7 are 6.4 m/s and 29.6°, respectively. Themaximum numbers of collocations are found in the middle tothe lower level of the atmosphere. The agreement betweensatellite winds and LAWPwinds is closer with respect to lowerSRMS values. During triple collocations, the SRMS andDRMS values (Tables 5 and 6) of both satellites are also almostidentical in nature. The number of collocations during DJF andMAM is less compared to that during the JJA and SONseasons in all tables because DJF (December 2010, 2011;January–February 2011) and MAM (March–April–May2011) consist of four and three months of data, respectively,while JJA (July–August 2010, June–July–August 2011) andSON (September–October–November 2010, 2011) consist offive and six months of data, respectively. The present valida-tion exercise confirms the potentiality of the usefulness ofsatellite-derived winds from Kalpana-1 to the level requiredfor assimilation into the numerical model for real-time weatherforecast in the tropical region. It should bementioned here thata combination of radiosonde and profiler wind measurementerrors and temporal and spatial variability of the atmosphericflow along with the error induced due to collocation matchcriteria may contribute to the differences of satellite-derivedwinds with radiosonde and profiler wind measurement. Thisstudy also showed, apart from the usual radiosonde observa-tion at few locations, the usefulness of winds from the profilerfor the validation of derived motion winds from geostationarysatellites.

Acknowledgments The authors are thankful to the anonymousreviewer for the critical and insightful comments and suggestionswhich have significantly improved the quality and presentation ofthe current version of the manuscript. The authors are also gratefulto the Meteorological and Oceanographic Satellite Data ArchivalCentre (MOSDAC) team (www.mosdac.gov.in) of SAC, ISRO,Ahmedabad, for putting Kalpana-1 AMV on operational basis, toNARL, Gadanki for providing wind data from LAWP, and to

NOAA for providing radiosonde wind available through the internetfor this validation exercise. The Director of SAC and the DeputyDirector of EPSA, SAC, ISRO, Ahmedabad, are acknowledged fortheir encouragement and help during the course of this study.

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